Establishing cause-effect is critical in the field of natural
resources where one may want to know the impact of management
practices, wildfires, drought, etc. on water quality and quantity,
wildlife, growth and survival of desirable trees for timber production,
etc. Yet, key obstacles exist when trying to establish cause-effect
in such contexts. Issues involved with identifying a causal hypothesis,
and conditions needed to estimate a causal effect or to establish
cause-effect are considered. Ideally one conducts an experiment
and follows with a survey, or vice versa. In an experiment, the
population of inference may be quite limited and in surveys,
the probability distribution of treatment assignments is generally
unknown and, if not accounted for, can cause serious errors when
estimating causal effects. The latter is illustrated in simulation
experiments of artificially generated forest populations using
annual plot mortality as the response, drought as the cause,
and age as a covariate that is correlated with mortality.We also
consider the role of a vague unobservable covariate such as 'drought
susceptibility'. Recommendations are made designed to maximize
the possibility of identifying cause-effect relationships in
large-scale natural resources surveys.